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Automatic modulation classification based on constellation density using deep learning
, M. Sheoran, G. Jajoo, S.K. Yadav
Published in Institute of Electrical and Electronics Engineers Inc.
Volume: 24
Issue: 6
Pages: 1275 - 1278
Deep learning (DL) is a newly addressed area of research in the field of modulation classification. In this letter, a constellation density matrix (CDM) based modulation classification algorithm is proposed to identify different orders of ASK, PSK, and QAM. CDM is formed through local density distribution of the signal's constellation generated using LabVIEW for a wide range of SNR. Two DL models, ResNet-50 and Inception ResNet V2 are trained through color images formed by filtering the CDM. Classification accuracy achieved demonstrates better performance compared to many existing classifiers in the literature. © 1997-2012 IEEE.
About the journal
JournalData powered by TypesetIEEE Communications Letters
PublisherData powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
Open AccessNo